Install litdata instead of lightning[data]

litgpt/data/openwebtext.py

@@ -46,7 +46,7 @@ def connect(
 
     def prepare_data(self) -> None:
         from datasets import Dataset, load_dataset
-        from lightning.data import optimize
+        from litdata import optimize
 
         if str(self.data_path).startswith("s3://"):
             print(f"The OpenWebText data path points to an S3 location: {self.data_path}. Skipping preprocessing.")
@@ -81,7 +81,7 @@ def tokenize(data: Dataset, index: int):
         )
 
     def train_dataloader(self) -> DataLoader:
-        from lightning.data.streaming import StreamingDataLoader, StreamingDataset, TokensLoader
+        from litdata.streaming import StreamingDataLoader, StreamingDataset, TokensLoader
 
         train_dataset = StreamingDataset(
             input_dir=self.data_path_train,
@@ -95,7 +95,7 @@ def train_dataloader(self) -> DataLoader:
         return train_dataloader
 
     def val_dataloader(self) -> DataLoader:
-        from lightning.data.streaming import StreamingDataset, TokensLoader
+        from litdata.streaming import StreamingDataset, TokensLoader
 
         val_dataset = StreamingDataset(
             input_dir=self.data_path_val,

litgpt/data/prepare_slimpajama.py

@@ -38,7 +38,7 @@ def prepare(
     chunk_size: int = (2049 * 16384),
     fast_dev_run: bool = False,
 ) -> None:
-    from lightning.data.streaming import DataProcessor
+    from litdata.processing.data_processor import DataProcessor
 
     tokenizer = Tokenizer(tokenizer_path)
     data_recipe = SlimPajamaDataRecipe(tokenizer=tokenizer, chunk_size=chunk_size)

litgpt/data/prepare_starcoder.py

@@ -12,7 +12,7 @@
 
 _LITDATA_AVAILABLE = RequirementCache("litdata")
 if _LITDATA_AVAILABLE:
-    from lightning.data.streaming import DataChunkRecipe
+    from litdata.processing.data_processor import DataChunkRecipe
 else:
     DataChunkRecipe = object
 
@@ -57,7 +57,7 @@ def prepare(
     chunk_size: int = (2049 * 8192),
     fast_dev_run: bool = False,
 ) -> None:
-    from lightning.data.streaming import DataProcessor
+    from litdata.processing.data_processor import DataProcessor
 
     tokenizer = Tokenizer(tokenizer_path)
     data_recipe = StarcoderDataRecipe(tokenizer=tokenizer, chunk_size=chunk_size)

litgpt/data/tinyllama.py

@@ -53,7 +53,7 @@ def prepare_data(self) -> None:
                 )
 
     def train_dataloader(self) -> DataLoader:
-        from lightning.data.streaming import CombinedStreamingDataset, StreamingDataLoader, StreamingDataset, TokensLoader
+        from litdata.streaming import CombinedStreamingDataset, StreamingDataLoader, StreamingDataset, TokensLoader
 
         train_datasets = [
             StreamingDataset(
@@ -79,7 +79,7 @@ def train_dataloader(self) -> DataLoader:
         return train_dataloader
 
     def val_dataloader(self) -> DataLoader:
-        from lightning.data.streaming import StreamingDataset, TokensLoader
+        from litdata.streaming import StreamingDataset, TokensLoader
 
         val_dataset = StreamingDataset(
             input_dir=self.slimpajama_val,

requirements-all.txt

@@ -6,12 +6,11 @@ sentencepiece             # llama-based models
 tokenizers                # pythia, falcon, redpajama
 datasets                  # eval
 requests                  # litgpt.data
+litdata                   # litgpt.data
 zstandard                 # litgpt.data.prepare_slimpajama.py
 pandas                    # litgpt.data.prepare_starcoder.py
 pyarrow                   # litgpt.data.prepare_starcoder.py
 tensorboard               # litgpt.pretrain
 torchmetrics              # litgpt.pretrain
 # eval
 git+https://github.com/EleutherAI/lm-evaluation-harness.git@115206dc89dad67b8beaa90051fb52db77f0a529
-# scripts/prepare_slimpajama.py, scripts/prepare_starcoder.py
-lightning[data] @ git+https://github.com/Lightning-AI/lightning@f23b3b1e7fdab1d325f79f69a28706d33144f27e

tests/data/test_openwebtext.py

@@ -8,11 +8,11 @@
 
 
 @pytest.mark.skipif(sys.platform == "win32", reason="Not in the mood to add Windows support right now.")
-@mock.patch("lightning.data.optimize")
+@mock.patch("litdata.optimize")
 @mock.patch("datasets.load_dataset")
 def test_openwebtext(_, optimize_mock, tmp_path, monkeypatch, mock_tokenizer):
     from litgpt.data import OpenWebText
-    from lightning.data.streaming import StreamingDataLoader, StreamingDataset
+    from litdata.streaming import StreamingDataLoader, StreamingDataset
 
     data = OpenWebText(data_path=(tmp_path / "openwebtext"))
     assert data.seq_length == 2048

tests/data/test_tinyllama.py

@@ -6,7 +6,7 @@
 
 def test_tinyllama(tmp_path, monkeypatch):
     from litgpt.data import TinyLlama
-    from lightning.data.streaming import StreamingDataLoader, StreamingDataset, CombinedStreamingDataset
+    from litdata.streaming import StreamingDataLoader, StreamingDataset, CombinedStreamingDataset
 
     data = TinyLlama(data_path=(tmp_path / "data"))
     assert data.seq_length == 2048

tutorials/pretrain_tinyllama.md

@@ -44,12 +44,12 @@ Around 1.2 TB of disk space is required to store both datasets.
 
 ## Prepare the datasets for training
 
-In order to start pretraining litgpt on it, you need to read, tokenize, and write the data in binary chunks. This will leverage our `lightning.data` optimization pipeline and streaming dataset that comes with Lightning.
+In order to start pretraining litgpt on it, you need to read, tokenize, and write the data in binary chunks. This will leverage the `litdata` optimization pipeline and streaming dataset.
 
 First, install additional dependencies for preprocessing:
 
 ```bash
-pip install 'lightning[data]' torchmetrics tensorboard sentencepiece zstandard pandas pyarrow 'huggingface_hub[hf_transfer] @ git+https://github.com/huggingface/huggingface_hub'
+pip install -r requirements-all.txt
 ```
 
 You will need to have the tokenizer config available: